Machine Learning Based Decision Support System for High-School Study

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dc.contributor.authorChaiwuttisak, Pornpimol
dc.date.accessioned2026-08-06T10:31:20Z
dc.date.available2026-08-06T10:31:20Z
dc.date.issued2021-01-01
dc.description.abstractThe objectives of this study are to investigate the correlations between personal factors, learning factors family, and economic factors affecting high-school study program selection and also to create and compare models of high-school study program selection with data mining techniques and to develop a decision support system for high-school study program selection with a data mining technique. Data were analyzed by five data mining techniques, and models of high-school study program selection were constructed. These models were then used to construct a decision support system from data mining software called RapidMiner Studio 9. The research findings were as follows personal factors, learning factors family, and economic factors affecting high-school study program selection, and from the result of high-school study program selection, the Decision Tree method, C4.5 algorithm provided the highest accuracy. Therefore, the researcher selected the forecasting model with the Decision Tree method, C4.5 algorithm together with the selection of features with the backward elimination method to create a decision support system.
dc.identifier.citationLecture Notes in Networks and Systems, 176 LNNS, 419-432, 2021
dc.identifier.doi10.1007/978-981-33-4355-9_32
dc.identifier.issn23673370
dc.identifier.other2-s2.0-85107354419
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/11691
dc.sourceLecture Notes in Networks and Systems
dc.subjectAccuracy
dc.subjectData mining
dc.subjectDecision support system
dc.subjectModel for selection of study program
dc.titleMachine Learning Based Decision Support System for High-School Study
dc.typeConference Paper

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